


import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__magic_name__  	=     '''\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'''

__magic_name__  	=     '''\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'''

__magic_name__  	=     '''\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION  )
class        lowerCAmelCase__ (					datasets.Metric  ):






						"""simple docstring"""




						def 	_UpperCamelCase					(       self      ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION ,				citation=_CITATION ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
							        {
							            "predictions": datasets.Sequence(datasets.Value("string" ,				id="token"      ) ,				id="sequence"      ),
							            "references": datasets.Sequence(
							                datasets.Sequence(datasets.Value("string" ,				id="token"      ) ,				id="sequence"      ) ,				id="references"      ),
							        }      ) ,				codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"] ,				reference_urls=[
							        "https://en.wikipedia.org/wiki/BLEU",
							        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
							    ] ,				)




						def 	_UpperCamelCase					(       self ,				a_ ,				a_ ,				a_=4 ,				a_=False      ):
							lowerCamelCase_					:				Tuple              =  compute_bleu(
							    reference_corpus=a_ ,				translation_corpus=a_ ,				max_order=a_ ,				smooth=a_      )
							((lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_)       ,(lowerCamelCase_))					:				List[Any]              =  score
							return {
							    "bleu": bleu,
							    "precisions": precisions,
							    "brevity_penalty": bp,
							    "length_ratio": ratio,
							    "translation_length": translation_length,
							    "reference_length": reference_length,
							}





"""simple docstring"""



import math
from datetime import datetime, timedelta
def      UpperCAmelCase     (       _lowercase :   int	)						-> datetime:


	"""simple docstring"""




	lowerCAmelCase_      							=			year % 1_9
	lowerCAmelCase_      							=			year % 4
	lowerCAmelCase_      							=			year % 7
	lowerCAmelCase_      							=			math.floor(year / 1_0_0	)
	lowerCAmelCase_      							=			math.floor((1_3 + 8 * leap_day_inhibits) / 2_5	)
	lowerCAmelCase_      							=			leap_day_inhibits / 4
	lowerCAmelCase_      							=			(
	    1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 3_0
	lowerCAmelCase_      							=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

	# days to be added to March 21
	lowerCAmelCase_      							=			(1_9 * metonic_cycle + secular_moon_shift) % 3_0

	# PHM -> Paschal Full Moon
	lowerCAmelCase_      							=			(
	    2 * julian_leap_year
	    + 4 * non_leap_year
	    + 6 * days_to_add
	    + century_starting_point
	) % 7

	if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_9	)
	elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_8	)
	else:
		return datetime(_lowercase			,   3			,   2_2	) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday	)	)


if __name__ == "__main__":
		for year in (19_94, 20_00, 20_10, 20_21, 20_23):
				lowercase_							=							'will be' if year > datetime.now().year else 'was'
				print(f"""Easter in {year} {tense} {gauss_easter(year)}""")